Employing associate memory learning agent for enhanced lifecycle management

ABSTRACT

A non-conformance analysis system may have an associative memory subsystem populated with information involving a plurality of entities defining different attributes of a component, with each entity being categorized under a user defined entity type, the entities and entity types forming an associative memory. A user input device may be used for enabling a user to input a query concerning the component, and to obtain information useful for managing a lifecycle of said component. An associative memory entity analytics engine in communication with the associative memory subsystem, and responsive to said user input device, searches the associative memory using the information provided in the query to retrieve entities helpful to the user in assessing the lifecycle of the component.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related in general subject matter to pendingU.S. patent application Ser. No. ______ (Attorney Docket No.7784-001153), filed concurrently herewith, entitled “Non-ConformanceAnalysis Using An Associative Memory Learning Agent”, assigned to TheBoeing Company, and hereby incorporated by reference in its entiretyinto the present application. The present application is further relatedin general subject matter to pending commonly assigned U.S. patentapplication Ser. No. ______ (Attorney Docket No. 7784-001157), filedconcurrently herewith, entitled “Associative Memory Learning Agent ForAnalysis Of Manufacturing Non-Conformance Applications,” assigned to TheBoeing Company, and hereby incorporated by reference in its entiretyinto the present application.

FIELD

The present disclosure relates to systems and methods for analyzing thelifecycle of a part, and more particularly to a system and method thatlearns the lifecycle of a part and then is able to use this memory orlearning to provide input to manage the part or similar parts throughouttheir their lifecycle.

BACKGROUND

The statements in this section merely provide background informationrelated to the present disclosure and may not constitute prior art.

Current parts lifecycle analysis systems and methods do not considercommon attributes between parts from the same or differentmanufacturers. For example, assume that a particular part ABC has aparticular coating, and that part ABC has an expected part lifespan orhas an identified non-conformance. It is currently not impossible toreview all of the parts in a system that may include the particularcoating because of the huge numbers and diverse nature of parts that usethe coating. This is especially so in complex manufacturing processes,for example in building commercial aircraft, where vast numbers of partsare involved in the construction of such an aircraft.

Thus, there presently is no means for focusing, in a rapid and orderlymanner, on those parts that may be similar to a particular part or thathave characteristics in common with the particular part. Thus, even ifone should determine that a part or a component of a subassembly havinga particular attribute should be reviewed, using that information toevaluate the lifecycle of similar parts or similar components of asubassembly has not been achieved.

Also, databases which have been used in the past for tracking partinformation have often been quite extensive in size. Such databasesoften may contain textual content that is input by a large number ofdifferent individuals, possibly designers, producers, operators,technicians, maintenance personnel, etc. As a result, differences invernacular used to describe characteristics of parts or other featuresof the components involved is very common. Thus, there exists acontinual challenge to extract pertinent information from large volumesof current and historical free text, which leads to a multitude ofcorrelation issues that add to the complexity of a part/componentlifecycle analysis. This also gives rise to a plethora of computationaland analytic problems. The usual result is long analysis and mitigationtimes which lead to high costs, which thus can be very burdening if notunacceptable for many businesses, governmental operations ormanufacturing entities. These limitations also make it difficult, if notimpossible, to use the available stored non-conformance information in aproactive manner to efficiently manage the lifecycles of parts orcomponents.

SUMMARY

In one aspect the present disclosure relates to a non-conformanceanalysis system. The system may comprise an associative memory subsystempopulated with information involving a plurality of entities definingdifferent attributes of a component, with each entity being categorizedunder a user defined entity type, the entities and entity types formingan associative memory. A user input device is used for enabling a userto input a query concerning the component to obtain information usefulfor managing a lifecycle of the component. An associative memory entityanalytics engine in communication with the associative memory subsystem,and responsive to the user input device, searches the associative memoryusing the information provided in the query to retrieve entities helpfulto the user in assessing the lifecycle of the component.

In another aspect the present disclosure relates to a method for forminga system for managing a lifecycle of a component. The method maycomprise using an associative memory subsystem populated withinformation involving a plurality of entities, with each entity beingcategorized under a pre-defined entity type, and the entity types andentities collectively forming an associative memory. An input device maybe used to enable a user to input a query concerning information relatedto managing a non-conformance of the component. An entity analyticsengine in communication with the input device and said associativememory subsystem may be used to analyze entities stored in theassociative memory and to retrieve specific ones of the entities storedin the associative memory that include information helpful to the userin managing a non-conformance of the component.

In still another aspect the present disclosure relates to a method formanaging a lifecycle of a component. The method may comprise providingan associative memory subsystem having an associative memory. Theassociative memory may have information relating to the component, theinformation being organized in accordance with pre-defined entity types,where each entity type has at least one entity representing a specificattribute.

An entity analytics engine may be used to search the associative memoryusing information provided from a user through a query. The entityanalytics engine may search to obtain all relevant entities containingattributes that may be related to the information in the query. Therelevant entities may be output to the user for analysis by the user.

Further areas of applicability will become apparent from the descriptionprovided herein. It should be understood that the description andspecific examples are intended for purposes of illustration only and arenot intended to limit the scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are for illustration purposes only and arenot intended to limit the scope of the present disclosure in any way.

FIG. 1 is a block diagram of a system in accordance with one embodimentof the present disclosure;

FIG. 2 is a flowchart illustrating one method for constructing thesystem of FIG. 1;

FIG. 3 is a diagram illustrating how the system of FIG. 1 may be used;

FIG. 4 is a diagram illustrating how the system may be used to obtainnon-conformance information concerning a specific non-conformance issueinvolving wing corrosion on an aircraft;

FIG. 5 is a flowchart specifically illustrating operations formed increating an associative memory for use with the present system; and

FIG. 6 is an illustration of one exemplary format in which entity typeand entity information obtained from the associative memory system maybe presented to the user for consideration;

FIG. 7 is a flow diagram illustrating operations performed by anotheraspect of the present disclosure that forms a method for managing anon-conformance of a component;

FIG. 8 is a flow diagram illustrating how a typical part may progressalong its lifecycle;

FIG. 9 is a flowchart illustrating operations performed using a methodof the present disclosure to perform a non-conformance analysis; and

FIG. 10 is a diagram of a plurality of exemplary entity types andentities that may be used in forming the associative memory to tailor itfor non-conformance analysis and component lifecycle analysis.

DETAILED DESCRIPTION

The following description is merely exemplary in nature and is notintended to limit the present disclosure, application, or uses. Itshould be understood that throughout the drawings, correspondingreference numerals indicate like or corresponding parts and features.

Referring to FIG. 1, there is shown a non-conformance analysis systemhaving an associative learning agent 10, and which will be referred tothroughout the following discussion for convenience as “the system 10”.Non-conformance may include any condition that is at variance with anominal condition, and may further include, for example, a suspectedanomaly, issue, test failure, or anomalies with a system or portionthereof.

The system 10 is suited for use in any application where non-conformanceanalysis of a system or process is required, and may be part of alifecycle management information system, tool, or methodology to assistpersons in making lifecycle-related decisions. While the system 10 isespecially well suited for large, complex systems and products, forexample the troubleshooting or maintenance of commercial aircraft, itwill be appreciated that the system may be adapted for use with muchsmaller and less complex systems, products and methods. The presentsystem 10 is therefore expected to find utility with a wide variety ofsystems, products and methods where rapid non-conformance analysis andnon-conformance identification is required.

Referring to FIG. 1, the system 10 may include one or more diverseindependent information storage tools where various forms ofnon-conformance information may be stored. Three such exemplaryinformation storage tools are illustrated as a wide area network 12 (forsimplicity simply “web 12”), one or more transactional databases 14 andone or more historical databases 16. However, it will be appreciatedthat any type of database or information storage system capable ofstoring useful non-conformance related information may be used with thesystem 10. Historical database 16 may be used to store historicalnon-conformance information concerning subsystems, component parts,vendors or any other criterion that may prove useful in non-conformanceanalysis. Transactional database 14 may store reports involving specifictypes of non-conformances previously investigated, for example assemblyissues or anomalies, test anomalies, reports by engineers or maintenancepersonnel on what action was taken to remedy an anomaly or even whatrepair action(s) had no effect on the anomaly. Transactional database 14may also contain usage information about the specific platform,subsystem or part; system logs that include platform usage (e.g.operating environment, number of cycles, hours of operation); as well ason-line standards documentation or trade journals that describepotential problems, non-conformance, or changes in materials ormanufacturing technology.

As another example of useful information that may be stored in one ormore of the databases 14 and 16, consider the situation when there is achange in the way a part for a subsystem is manufactured, and then atsome future time other users (or even users in a different industry)discover a potential problem and report information pertaining to thepotential problem. It might take considerable time to determine that apotentially pervasive problem may exist that is common acrossindustries. As one specific example, assume that the coating on a wirebundle was changed to reduce potential environmental damage. The wirewith coating is used by many different industries. Now assume that it istypical to mark with stickers to identify (ID) the different wire endsto ID the destination component during manufacturing. Now also assumethat the sticker glue is not compatible with the new formulation of thewire bundle coating and causes the wires to short. Now an individualworking in one industry discovers a potential problem and reports theissue in a trade journal. If the relevant information from the tradejournal is stored in one or more of the databases 14 or 16, a user in adifferent industry facing the same or a similar potential problem mayuse the system 10 to query and learn all the pertinent informationstored that concerns this specific subsystem. The attributes of a givensubsystem that makes use of the wire bundle may include that it is madeup of this specific type of wire (e.g., part of the bill or materialsassociated with every subsystem). And because it has this wireattribute, that specific wire will associate this subsystem with thejournal entry that describes potential problems associated with thespecific subsystem that uses the wiring bundle.

Thus, it will be appreciated that non-conformances requiring analysis bythe system 10 may occur in manufacturing processes, with independentcomponents or parts, with coatings, with raw materials, or may be causedby individuals or even teams of individuals. As such the system 10 maybe supplied with whatever form of information or data that may behelpful in performing a non-conformance analysis investigation. Theinformation tools 12, 14 and 16 may also include textual content thathas been supplied by a plurality of designers, engineers, scientists,producers, operators, technicians, maintenance personnel and othercontributors, so differences in documentation approach, terminology,vernacular and even spelling of non-conformance conditions and relatedinformation may be present.

With further reference to FIG. 1 the system 10 also may include a datamining tool 18 and an associative memory subsystem 19. The associativememory subsystem 19 may include an associative learning memory 20(hereafter simply “associative memory 20”) and an associative memoryentity analytics engine 21 (hereinafter the “entity analytics engine21”). An input device such as a computer display terminal 22 maycommunicate bidirectionally with the entity analytics engine 21. Theassociative memory 20 is in bidirectional communication with the entityanalytics engine 21. The entity analytics engine 21 may make use of oneprocessor, but more typically a plurality of processors, that operate inconnection with entity analytics query software 21 a to perform queriesfor information stored in the associative memory 20. The entityanalytics engine 21 receives non-conformance queries from a user 24 viathe computer display device 22 and the query software 21 a and controlsthe generation of the pertinent entity types and entities for a giveninput query by the user. The entity analytics engine 21 converts thewords in the non-conformance input query into attributes and retrievesall of the specific entities (relating to various different entitytypes) that have information that meets one or more of the attributesrelating to the non-conformance input query. Any such information isretrieved from the associative memory system 20. By the terminology“attribute” it is meant any piece of knowledge or characteristic such asadjectives, verbs, nouns (e.g., “yellow”, “rust”, “bent”, “dented”,“nut”, “bolt”, “corrosion”); any part number, any process step, anymanufacturer name, any assembly line number or build date, any technicalor service bulletin, etc., that relates to the non-conformance inputquery.

A database update software system 23 is used to update the informationtools 12, 14 and 16 with any documents created by the user, such asreports concerning a successful fix of a non-conformance beinginvestigated, or any other information that the user wishes to inputthat may be of interest in future non-conformance analysis related tothe same or similar analyses. The entity analytics engine 21periodically updates the associative memory 20 with new informationretrieved from information tools 12, 14 and 16 so that the associativememory 20 will contain all of the entity information available to thesystem 10 when the system is next accessed for use by a user.

Prior to a first use of the system 10, a system designer defines atleast one entity type, but more typically a plurality of entity typesthat relate to specific categories of information that may be used tohelp evaluate a non-conformance for a specific application. These entitytypes are mapped into the associative memory 20. The data mining tool 18identifies a plurality of entities as it reviews all of the informationavailable in the information tools 12, 14 and 16 and sends theidentified information to the associative memory 20 for storage. Thus,each specific entity type may have associated with it at least one, butmore typically a plurality of different specific entities. Depending onthe application that the system 10 will be used with, dozens, hundredsor more entity types may be defined by the system designer to identifycategories of information that may be useful in helping the user toanalyze a non-conformance condition. For example and without limitation,entity types may be the names of vendors that supply component parts;mobile platform models; types of parts (e.g., fastener, spring, etc.);the names of customers that own the device or mobile platform beinganalyzed for a non-conformance; the names of subsystems of the mobileplatform, device or system that is the subject of the non-conformanceinvestigation; specific serial numbers of vehicles, subsystems or parts,etc. Entity types can thus be thought of as different categories ortypes of information (or even different ways or perspectives to rememberthe information) that may be useful in the non-conformance analysisprocess.

The specific entities of a given entity type can be thought of asspecific objects or groupings that may represent specific items orcomponents related to the application or business. For example andwithout limitation, an entity type of “fastener” may have severaldifferent entities associated with different part numbers for different,specific fasteners. Thus, for an entity type of “fasteners”, differententities might exist for a specific style/type of rivet, a specificsized threaded bolt; a specific size of cotter pin, a specific sizednut, etc. One entity would be created for every fastener used by abusiness. In this example one, entity is created for every differenttype of fastener that the business uses. As another example, a specificentity type may be created for an “aircraft model”, and may have severalspecific entities associated therewith that each specify a different,specific model of aircraft. As a further example, a specific entity typecalled “serial number” might be created, and it may have a number ofspecific, different entities associated therewith that each list aspecific serial number. So a free text query by the user fornon-conformance information concerning a specific serial number of apart or subsystem may be input to the system 10 by the user and theassociative memory query software 21 a will search the associativememory 20 for entities stored therein that have a relationship to thatspecific serial number.

The computer display terminal 22 may be used by a user of the system 10to input free text queries to the associative memory 20 that pertain tothe non-conformance being diagnosed. For example, a free text querymight comprise a statement such as: “Wing flap corrosion beingexperienced on model XXX aircraft manufactured at ZZZ manufacturer atCity/State” that is input through the computer display terminal 22 tothe entity analytics engine 21. The ability to receive free text inputsis a significant advantage of the system 10 because it enables all datadefining the potential problem to be used in the lifecycle analysis.Even a word in the free text query such as a noun (e.g., “overtightened”, “frayed”, “worn”, “broken”, “bent”, “burned”, etc.) canrepresent an attribute that contributes to the entity analytics engine21 finding related entities stored in the associative member 20 that mayhelp the user with his/her non-conformance investigation. However, evenstructured information, such as simply a part number or model number,could be entered as the non-conformance query.

Another significant advantage of the system 10 is that it does not makeuse of reductive algorithms, which can actually eliminate some portionsof input information that describe or characterize the non-conformancethat could be helpful in troubleshooting the non-conformance condition.Such reductive algorithms typically categorize non-conformances intospecific categories (e.g. related to the electrical system, passengercompartment, lighting system, etc.). Thus, the user is able to learn howmany “types” of related non-conformance issues may have been previouslysearched.

Referring now to FIG. 2, a flowchart 100 is shown of operations that areperformed by the system 10 during a non-conformance analysis. Atoperation 102 the specific non-conformance is defined by the user viathe computer display terminal 22. Again, as an example, a specificnon-conformance issue might be defined in free text form as a text entrythat reads: “Wing corrosion on a model XXX aircraft manufactured by ZZZcompany”. Alternatively, the user may enter a model name of a mobileplatform, a part number of a specific part under investigation; aspecific serial number of an assembly under investigation, etc. For thepurpose of this example it will be assumed that the user provides a freetext query.

The system 10 uses the entity analytics engine 21 to perform entityanalytics searches on all of the words that make up the free textnon-conformance query, as indicated at operation 104. The entityanalytics engine 21 will recognize some words as entities and some assimply attributes, but will use each word in the search query insearching for every entity that may have some association with each wordin the search query. At operation 106 the entity analytics engine 21searches the associative memory system 20 to retrieve information havingspecific attributes for each associated entity. This search is performedwith a focus on how the attributes are associated with thenon-conformance being investigated. This operation is repeated withsuccessive queries by the entity analytics engine 21, as indicated byquery 108, until all the relevant entity and entity type information iscompiled. At operation 110, the system 10 may generate a report of thesearch results that is sent to the computer display terminal 22 fordisplay. This report would include all of the information associatedwith all entity types and all of the entities for each entity type.Typically this information may be generated within a few seconds or lessfrom the time the user enters a description of the non-conformance beinginvestigated. At operation 112 the information tools 12, 14 and 16 maybe updated via the database update software system 23 with anyinformation that the user has created after reviewing the entity typeand entity information. At operation 114 the entity analytics engine 21may update the associative memory 20 with any new information that wasstored in the information tools 12, 14 and 16. This updating may involvepopulating existing entities with additional specific information oreven creating new entities (e.g., by adding part numbers of additionalparts, as new entities, that have been discovered to be pertinent to theperformance or lifecycle management of a particular subsystem orcomponent).

Referring now to FIG. 3, a diagram 200 of an operational flow of thesystem 10 is shown. At operation 202, prior to the first use of thesystem 10, the entity types that may be pertinent to a lifecycleanalysis and management for a specific application are defined for theassociative memory 20 by the system designer. Thus, for example, fornon-conformance analysis of a specific commercial aircraft, a specificentity type might be of the major subsystems of the aircraft. This wouldassume that there could be non-conformance issues that can be resolvedby looking at all the information associated with a major subsystem andnot just at individual parts. Such an assumption would likely be anaccurate one in this example, because it may be that non-conformanceissues are clustered around one or more particular subsystems. Forexample, assume the user begins investigating a specific example ofdelamination. The user could enter a free text query into the computerdisplay terminal 22 with the term “delamination”. If most of thedelamination issues found by the system 10 were related to a certainsubsystem (e.g., a tail assembly), then the tail assembly entity couldbe displayed to the user.

At operation 204 the data mining tool 18 identifies and accesses all ofthe information tools to find and retrieve information having attributesthat may form specific entities, where the specific entities relate toone or more of the newly defined entity types. At operation 206, all ofthe retrieved entities have their attributes correlated with one or moreof the previously defined entity types and stored to form theassociative memory 20. The retrieved information may thus involvehistorical non-conformance data concerning specific subsystems as wellas specific components or parts of specific subsystems. Other exemplaryhistorical data could involve historical repair information, subsystemuse data, planned and unplanned maintenance actions and information, andservice advisories, just to name a few. Various well known data miningtools exist for this purpose. For example, suitable data mining toolsare available from SRA International, Inc. from Fairfax, Va.

At operation 208, when a non-conformance is to be investigated, a userenters pertinent information as free text or as structured data into thecomputer display terminal 22. At operation 210, the entity analyticsengine 21 of the associative memory 20 analyzes all of the terms orstructured data input by the user at operation 208, and determines theentity types and entities for which information needs to be obtainedfrom the associative memory 20. Essentially, the associative memoryqueries performed by the entity analytics engine 21 involve successivequeries of the associative memory 20 to obtain all of the relevantinformation pertaining to the selected entity types and entities. Forexample, one associative memory query may focus on the word “corrosion”that is part of a free text entry by the user describing thenon-conformance condition to be investigated. The entity analyticsengine 21 would retrieve all of the pertinent entities types and thespecific entities that correlate with the non-conformance informationprovided by the user. This operation may be viewed as a“knowledge/discovery” operation in which the query software 21 a of theentity analytics engine 21 accesses the associative memory 20 to findand extract all of the pertinent, saved non-conformance informationavailable in the associative memory 20 that pertains to the entity typesand entities that it has selected. The obtained information is thenpresented in a logically organized format by the entity analytics engine21 to the computer terminal 22, as indicated by diagram 212.

Importantly, the entity analytics engine 21, through its repeatedsearching of the associative memory 20, returns information that alsoindicates how well correlated the retrieved entities and entity typesare with the non-conformance information provided. As one example, theentity analytics engine 21 may indicate with a numerical value how manytimes a specific entity came up during the multiple memory queries thatwere performed by the entity analytics engine 21. Alternatively, theentity analytics engine 21 may provide other information that indicatesmore generically how strongly each of the retrieved entities and entitytypes are correlated with the non-conformance information input by theuser. For example, the strength of correlation of each specific entitycould be represented to the user through the use of different colorswhen displaying the specific entities that are retrieved. For example,if a particular entity came up only once, then the color white could bethe background used to display that particular entity on the computerdisplay terminal 22. However, any entity that came up three times ormore could be displayed with a red background. These colors could ofcourse be used in addition to numbers to indicate the exact frequencythat each particular entity came up. Another alternative to helpillustrate the strength of correlation could be the use of differentfont sizes for numbers displayed for each specific entity. For example,if a specific entity came up only once, it might be displayed in 10point type size, but any entity that comes up three times or more couldhave a number associated therewith that is displayed in 16 point size.The resulting entity types and entities retrieved by the entityanalytics engine 21, as well as the correlation information it provides,thus present the user with disparate ways (i.e., one for every relatedentity type) in which to view and investigate the specificnon-conformance being analyzed. An example of an entity analytics engineavailable commercially is “SAFFRON ENTERPRISE™” available from SaffronTechnology of Morrisville, NC. It will be appreciated that the entityanalytics query software 21 a will be constructed by the system designerto recognize those words, numbers or even characters that are importantin the specific type of application that the system 10 is being usedwith.

Referring now to FIG. 4, an operational diagram 300 is provided to helpillustrate a specific example of how the system 10 operates. Thespecific example relates to the “Wing Flap Corrosion” non-conformancementioned earlier herein. At operation 302, a free text query may beentered by the user designating “Wing Flap Corrosion” as thenon-conformance to be investigated. At operation 304 the entityanalytics engine 21 sequentially performs a plurality of queries toretrieve from the associative memory 20 the information that pertains tothe entity types and specific entities previously selected. The entitytypes are arranged in rows in this example denoted by reference numbers306 ₁-306 _(n). Thus, in this example the entity types “Part #”, “Serial#”, “Customer Name”; “Line #”; “Manufacturer Name”; and “Model #” areretrieved as all of the pertinent entity types. In actual practice,however, typically dozens, hundreds or more entity types may beretrieved that all relate to some attribute of the non-conformanceinformation that the user has provided through his/her free text query.All of the entities 308 ₁-308 _(n) associated with each of the entitytypes 306 ₁-306 _(n), respectively, are also retrieved. A number may beprovided with each entity 308 ₁-308 _(n) indicating the number of timesthat each specific entity turned out to be involved in previousnon-conformance investigations. For example, number “9” in the entitybox 308 ₄ in FIG. 4 might denote that a specific part number wasinvolved in previously investigated wing corrosion non-conformanceinvestigations a total of 9 times. Similarly, the number “30” in entitybox of the “Customer Name” entity type row 306 ₃ would indicate that aparticular customer was somehow involved on 30 occasions with the wingflap corrosion issue being investigated. Likewise, the number “4” in row306 ₄ would indicate that a particular assembly line was involved fourtimes with the wing flap corrosion issue being investigated. Thus, eachentity file or record, (which may for convenience simply be termed an“entity box”) in every entity type row 306 ₁-306 _(n) represents aspecific entity, or put differently, a specific piece of informationthat falls within a specific entity type. An entity 308 ₁-308 _(n) thatdoes not have a number indicates that it is not associated with thenon-conformance information provided by the user. Each entity box mayhold a variable amount of information, for example hundreds of megabytesof information, concerning that specific entity. For example, in anaircraft application, the information might be all the informationassociated with a specific aircraft model. In another example, an entitybox might contain a few bytes of information associated with a part thathas never been identified as having worn out and is not stocked. When auser does a query the entity analytics engine 21 looks at every entitybox (from every entity type) and looks for the entity box that hasinformation (associated entities and/or attributes) that best match theinformation (entities or attributes) in the user's query. The bestmatches are retrieved and sent to the user.

At operation 310, the information collected from the associative memory20 at operation 304 may be summarized in a user friendly format to theuser, possibly in a printed report or on the computer display terminal22. From the entity type information, the entity information, and thenumbers associated with the occurrence frequency of each specificentity, the user is able to quickly assess which entities may be highlypertinent to resolving the specific non-conformance investigationundertaken. The associative memory 20 effectively retrieves all types ofpreviously stored information that may have a bearing on the specificnon-conformance being investigation, as well as retrieving informationon specific entities of each entity type that have previously beenassociated with a similar non-conformance seen in a priornon-conformance investigation.

Referring to FIG. 5, a flowchart 400 is shown of operations that may beperformed to form an associative learning memory. At operation 402 aplurality of entity types are defined. At operation 404 data mining ofpreviously stored information from a plurality of information tools isperformed to obtain specific information relevant to the entity typesthat are defined for use in the associative memory 20. At operation 406specific information (entities and attributes associated with thoseentities) obtained during the data mining operation is stored in theassociative memory 20. At operation 408, analytics are used to analyzethe stored, specific non-conformance information and to retrievespecific ones of the entities (of various different entity types) thatinclude information pertinent to the specific non-conformance analysisbeing undertaken. At operation 410, the obtained entities and theinformation associated with those entities may be displayed on asuitable display, for example on computer display terminal 22.

Referring to FIG. 6, one exemplary arrangement is shown in diagram 500for presenting the entity and entity type information obtained from theassociative memory 20 searching to a user. It will be appreciated thatthis information may be displayed on the computer display terminal 22 orpossibly just printed out from a printer (not shown) in communicationwith the computer display terminal 22. The diagram 500 shows an“Attributes Cloud” box 502, an “Associated Entities” box 504, an“Associated Parts” box 506 and a “Snippets” box 508. The AttributesCloud box 502 lists attributes (i.e., represented by words) that relateto any of the terms input by the user in the initial non-conformancequery. One particular attribute, “removal”, is shown in bold print tosignify that this attribute came up more frequently than the otherattributes as the associative memory 20 queries were performed by theentity analytics engine 21 on the content stored in the associativememory 20. The “Associated Entities” box 504 shows the entity typespulled up from the associative memory searching 20 as being airline“Operators” and “Line Number”. The four specific airlines that came upduring the searching (i.e., “XYZ Airlines”, “ABC Airlines”, “QRSAirlines” and “EFG Airlines”) imply that these specific four airlinesare relevant to one or more of the search terms used in thenon-conformance query input by the user. Entity types “ATA Chapter” and“Model” also came up, along with several specific entities for each(e.g., specific aircraft model numbers for the “Model” entity type).This means that these specific entities are involved with, or match, oneor more of the search terms used in the non-conformance query. The“Associated Parts” box 506 lists specific part numbers (which representspecific entities) that came up during the associative memory 20searching that are somehow connected with, or correlated to, one or moreof the search terms used in the initial non-conformance query. The lastnumber (i.e., s283U000-10) is shown in enlarged and bold face print,indicating that it came up more frequently than any other part numberduring the associative memory 20 searching. The “Snipits” box 508provides short summaries of particular reports that came up in theassociative memory 20 searching, and that involve one or more of thesearch terms used in the non-conformance inquiry input by the user. Fromthis collective information, the user is able to quickly focus in onthose entity types and specific entities that have a direct bearing onthe non-conformance being investigated.

The system 10 and method described herein provides a number ofsignificant advantages over previously developed relational databasesthat have traditionally been used for non-conformance investigation andanalysis. A central overall advantage of the system 10 is its ability torapidly correlate multiple sources and multiple formats of informationaldata—including free text formats—and present it in such a way that auser can rapidly and accurately identify and effectively manage thelifecycle of one or more parts through intelligent queries based onsubject matter experts choosing the set of entities.

The system 10 can also answer other related questions withoutre-building the structure of the memory entities. The advantage ofassociative memory/entity analytics (“AM/EA”) implementations is thatbecause one can have such a large number of different entity types, onecan answer and discover different questions. In the associative memory20 of the present system 10, when something is “observed” by the system10, it is recorded in the associative memory 20 for every related entitythat is observed as being related to the non-conformance informationbeing input by the user. In a relational database the information istypically stored only once in the location chosen by the databasedesigner. Because it is stored in one way it can be accessed only in oneway, typically through the key that defines that specific table in arelational database.

Another significant advantage of the system 10 is that the system 10 mayuse all available non-conformance information/data provided by the userwhen performing the associative memory 20 queries. It does not, as otherpreviously developed systems typically do, store a summarized version ofthe information being input by the user in order to reduce the scope ofthe query (i.e., the scale of the searching that will be done) tosomething that works with the technology, and in so doing lose importantor otherwise useful facts present in the source data. Existingrelational database manipulation tools can find keywords, but theperspective is always that of the relational database designer, not theentity that relates to the current query. Relational databases alsogenerally do not account for all the entities that reside in the freetext information provided by the user. Relational databases further areoften slow and difficult to manipulate. The present system 10 cantypically provide responses to user inputs within a second or less,while a typical relational database may require significantly longertimes to search and obtain relevant, stored non-conformance information.

The system 10 also does not rely on rules based systems, which also maysuffer from the drawback of eliminating potentially useful portions ofthe information contained in the initial non-conformance inquiry made bythe user. This is because the “rules” that are used to find “relevant”data by their very nature limit the flexibility of the system to theimplemented rules. The other drawback with rules based systems is thatthe number of rules required will grow with the size of system and theincreasing quantity and types of information that must be accessed andsearched. So as a rules based system grows larger and larger, it becomesmore unmanageable.

Referring to FIG. 7, another aspect of the present disclosure involves asystem and method that is well suited for performing non-conformanceanalysis and component lifecycle analysis. It will be appreciated thatthe system 10, while being explained in connection with FIGS. 1-6 as anon-conformance investigation system, is also well suited to beconfigured, with little or no physical hardware modification, for use asa non-conformance and lifecycle managing system for managingnon-conformances or the lifecycles of components, subsystems, parts andother items. The ability to proactively determine non-conformance orlifecycles of components can form a significant benefit in reducing thedown time of equipment, systems or mobile platforms whose operationmight otherwise be significantly affected by a component that wears outunexpectedly. These and other benefits of the system 10, when configuredas a non-conformance analysis and management system, will be describedmore fully in the follow paragraphs.

Referring initially to the flow diagram 600 of FIG. 7, the basic phasesor operations that may be performed in conducting a non-conformanceanalysis or component lifecycle analysis are shown. At operation 602 anhistorical database may be accessed to provide information to a memoryforming device. At operation 604 the memory forming device may be usedto populate an historical associative memory subsystem, as indicated atoperation 606. The historical associative memory subsystem may be formedby associative memory subsystem 19 of FIG. 1. The memory formingoperation 604 may comprise using a data mining tool, such as data miningtool 18 of system 10, that periodically accesses the historical databaseto update the historical associative memory with any new informationthat has been added to the historical parts database.

At operation 608 a user first identifies a component or attribute of acomponent that has a particular non-conformance or is suspected as beingrelated to another part having a particular non-conformance. Atoperation 610 the information concerning the component or attribute ofthe component is analyzed, for example using the entity analytics engine21, which accesses the historical associative memory to find allrelevant entity information stored in the historical associative memoryconcerning the component or attribute in question. At operation 612 anadditional entity analytics analysis is performed to determine entityinformation for the same component being investigated by the user, aswell as entity information for components that are similar to thespecific component that the user is investigating. For example, the usermay be investigating the lifecycle of a specific type ofelectrohydraulic actuator used to move a particular flight controlcomponent on a commercial aircraft. Having lifecycle informationconcerning the specific, selected actuator would be highly useful inmanaging its lifecycle. And it would also be helpful to the user to havenon-conformance information concerning similar electrohydraulicactuators used to control other components of the aircraft. Suchadditional information concerning similar actuators would likely bevaluable to the user in enabling the user to perform a broader, morecomprehensive lifecycle analysis for the specific actuator underconsideration. At operation 614 the retrieved entity information may bepresented to the user in a report format, for example using the computerdisplay terminal 22. Alternatively, a printed non-conformance analysisor component lifecycle report could be provided to the user.

The ability to proactively manage the lifecycle of a component (i.e.,replacement before the component wears out) depends in part onunderstanding the stages of a component as is progresses from the designstage to the time at which it wears out. This is important because acomponent that is approaching the end of its lifecycle, even though ithas not worn out yet, but has followed a similar pattern or use as apart that has worn out, could have a similar lifecycle to the same orsimilar type of component that has worn out. Thus, understanding thelifecycle progress of various parts can be extremely useful in managingthe replacement of components that wear out. The flow diagram 700 ofFIG. 8 is intended to help illustrate an exemplary lifecycle for acomponent. At stage 702 the component in question may be designed inaccordance with a particular specification, for example in accordancewith a specific weight, material, using a particular coating, or beingsuitable for use in a specific location and/or environmental condition(e.g., temperature, exposure to environmental elements such as rain,ice, etc.). At stage 704 the component may be manufactured by aparticular vendor. At stage 706 the component may be released to aparticular contractor. At stage 708, at some future time the componentmay be identified as possibly damaged or malfunctioning. At operation710 the component may be repaired.

In practice the component may be used on various different systems ormobile platforms, for example on different aircraft of a particularairline. At stage 710 the repaired part may be the subject of a newcontract for a different system or mobile platform. Or it may be thatthe repaired part is reinstalled on a different system or differentcommercial aircraft from the one in which it was initially used with. Atoperation 712 the part may be the subject of a contract with anothermanufacturer. At operation 714 the price of the part may be changed.This could be an example of an event that has no impact on the lifecycleof a part, but it might also be an indication of a vendor “dumping” apart because a better one is being released soon. Every type of partfrom every industry has the potential of having a different lifecyclewhere some of the information associated with the part is significantand other information is less significant (i.e. “just noise”) from thelifecycle perspective. Using the associative memory 20 and entityanalytics engine 21, and treating the part or the subsystem as anentity, makes it possible to “learn” or remember a pattern that leads upto a point where analysis is needed and replacement may be recommended.The lifecycle is stored in the entity and the entity analytics engine 21only has to look for an entity (or entities) with significantly similarhistories or lifecycles (whatever those histories are). It is possiblefor parts types to share one lifecycle. Is also possible that a part canhave several lifecycle possibilities. Hence, it is impossible to predictthe lifecycle of a particular part, component, subsystem, or system withcertainty. At stage 716 the part may be identified has having another orsimilar non-conformance again.

From the foregoing diagram of FIG. 8 it will be appreciated that asizeable portion of potentially valuable non-conformance analysis (orlifecycle) information occurs post-production of the component. In areal world commercial aircraft manufacturing application it may be thata new manufacturer has applied to the Federal Aviation Administration tomanufacture the part after a first manufacturer has been manufacturingthe component for some time. And it may be that the component has a longlead time to manufacture (e.g., may require complex tooling to becreated before manufacturing can begin). All of this information may bevaluable in assessing a non-conformance or lifecycle of the component,as well as identifying what other subsystems, manufacturers, mobileplatforms, etc., may be affected or involved with the component inquestion. Accordingly, it should be appreciated that being able tomanage the lifecycle of a component has wide ranging benefits to notjust the original manufacturer of the component, but potentially to manyother entities that have products, systems or equipment that make use ofthe component or similar component in question.

Referring now to FIG. 9, a flowchart 800 is shown of operationsperformed in forming a non-conformance or lifecycle prediction system.At operation 802 a system designer uses entity analytics engineeringprinciples to identify all the relevant entity types that will be usedto categorize the information stored in the associative memory system,such as system 20 in FIG. 1. For the purpose of providing one specificexample, assume that the component part that the system designer isconsidering is an actuator. The entity types defined by the systemdesigner may involve attributes such as, without limitation, partnumbers for different types of actuators; manufacturers of all of theactuators defined by the system designer; subsystem names in which allof the various actuators defined are used; equipment or vehicle models(such as aircraft) on which the defined actuators are employed; thenames of other components that potentially could be viewed as similar orequivalent in function or operation to the actuator being considered;locations on a vehicle where the actuator is used; environmental factors(e.g., heat, cold, rain, etc.) that the actuator is designed to operatein, and essentially any other factors that may be pertinent to thenon-conformance analysis or lifecycle analysis of the actuator. Theentities for each entity type may define selected attributes of itsassociated entity type. For example, an entity type of “Aircraft Model”may have a plurality of entities associated therewith defining aplurality of different models of the aircraft that the actuator has beenused on. Another example could be an entity type of “Subsystem” whichmay have a plurality of different entity types each defining differentspecific subsystems of an aircraft that the actuator is used in. Stillfurther, an entity type of “Similar Components” may have a plurality ofdifferent entities associated therewith that each name different devicesor parts that one might reasonably view as being similar to, orequivalent to, the actuator in question. Still further, another entitytype might be “Location”, which might have a plurality of entitiesassociated therewith that define specific different locations where theactuator is used on an aircraft or other structure or vehicle. Theforegoing is not intended to be an exhaustive list, but merely toprovide an appreciation of the wide ranging and diverse nature of theentity types and entities that may be defined by the system designer tomeet the needs of a specific manufacturing enterprise or other form ofapplication. At operation 804, an associative learning memory is formed,for example associative memory 20, by using the data mining tool 18 tomine information from various information tools, such as historicaldatabases (e.g., historical databases 16), transactional databases(e.g., transactional databases 14) and possibly a wide area network(e.g., wide area network 12). The mined information is used to form theentities and is categorized under the user-defined entity types. Theentities are stored in an associative memory system, such as associativememory 20.

At operation 806 an entity analytics engine, such as entity analyticsengine 21, is used to receive an entity that is of interest, forexample, an entity mentioned as a term in a search query input by theuser. In one example the entity may be for a part number, subsystem, orsystem that has a reported non-conformance.

At operation 808 the entity analytics engine 21 performs searches ofinformation stored in the associative memory system 20 to obtain allentities that are similar to the entity under investigation. Any storedobject of information that is the same or similar to the entity ofinterest, whether in form, function or lifecycle, may be obtained by theentity analytics search engine 21. For example, if the entity is a partnumber then the “similar to” query will return part numbers that havethe similar attributes as the part of interest. The query will alsoreturn the attributes that matched so that the user can determine ifthey are significant. For example, if corrosion is found on a part ofinterest, and attributes associated with the part of interest includedmaterial make up, operating environment, repair history, or in-serviceyear, then it is possible to use this information to look for (oranticipate) that there might be other parts working in the sameenvironment, with the same maintenance schedule, and made of the samematerial, that may be investigated to determine if they may develop thesame corrosion issue.

At operation 810 the entity analytics engine provides all of theretrieved information pertaining to the entities which it has deemedsignificantly similar to the queried entity. This information may bedisplayed on a user input device (e.g., input device 22), oralternatively printed out in a report or provided to the user in anyother format or medium that is easy to study. Finally, at operation 812the user may use the input device or any other subsystem to update theinformation tools (e.g., components 12, 14 and 16) with reports based onother similar parts that are either further ahead or behind in thelifecycle of the part in question. The user may also input any otherreports that pertain to the non-conformance analysis or lifecycleinvestigation just undertaken, and which might be helpful in a futureanalysis of the same or a similar part.

Referring now to FIG. 10 a logic diagram 900 is shown to illustrate anexemplary collection of entity types and entities that may be used in anon-conformance or lifecycle prediction system. In this example it willbe assumed that the non-conformance analysis system is being used in anaircraft manufacturing or maintenance environment. In this example theentity types 902 ₁-902 ₆ “PART #”; “Subsystem”; “MANUFACTURERS USINGPART”; “AIRCRAFT MODEL”; “LINE #s”; and “Non-Conformance Report” havebeen selected by the system designer for the associative memory 20. Eachentity type is shown as including a plurality of boxes that indicatespecific entities associated with that particular entity type. Thus,entities 904 ₁-904 ₅ are associated with entity type 902 ₁; entities 906₁-906 ₅ are associated with entity type 902 ₂; entities 908 ₁-908 ₅ areassociated with entity type 902 ₃; entities 910 ₁-910 ₅ are associatedwith entity type 902 ₄; entities 912 ₁-912 ₅ are associated with entitytype 902 ₅; and entities 914 ₁-914 ₅ are associated with entity type 902₆. In practice there may be dozens or more entity types that areselected by the entity analytics search engine 21 depending on the inputinformation that forms the non-conformance analysis query. The numbersin the entity boxes 904-914 indicate the number of times that eachentity came up during the searching process. Thus, by looking atinformation concerning the life spans of components that are identicalor similar to the component under investigation, one can moreeffectively manage the lifecycle of the component under investigation.User reports or documents concerning lifecycles of certain components,that are entered into the information tools 12, 14 and 16, enable thesystem to effectively “learn” more and more about the lifecycles ofvarious components as the system 10 is used is more and more over aperiod of time.

The system 10 thus uses entity analytics to provide a new and originalway to identify and manage component and system lifecycles, or othersignificant events in the production and use of complex systems. Thesystem 10 is applicable to any scale commercial or governmental systemor process. However, its value increases significantly as the size andcomplexity of the system or product increases since the productlifecycle management process becomes exponentially more difficult as thesize and complexity of the system or mobile platform with which thecomponent increases. Still another advantage is the ability to rapidlycorrelate multiple sources and multiple formats of informational data,including free text formats. Yet another advantage is the ability of thesystem 10 to present the retrieved information in such a way that ananalyst can rapidly and accurately manage a part through its lifecycleusing intelligent queries based on subject matter experts havingpreviously chosen an optimal set of entities. A report may be generatedtelling the designer or quality engineer analyst or maintainer thatfurther analysis is indicated for a given part, and a part or system maybe maintained more efficiently.

Still another advantage of the system 10, when configured for componentnon-conformance analysis and lifecycle analysis, is that the systemmakes comparisons across entities, and not using just one database. Thesystem 10 is able to arrive at solutions faster than previouslydeveloped database systems because it integrates data from multiple datasources. The system 10 is also able to answer other related questionswithout re-building the structure of the memory entities. For example,when learning information from one data source about non-conformances orother issues, many different entity types can be updated. For example,if non-conformance reports are added to the system it is possible toremember the non-conformances associated with parts, users, models andline numbers at the same time. Then the system 10 can be used to findparts that have the same lifecyle (part material, maintenance, location,use) and aircraft line numbers that have the same lifecycle For example,consider if the same four parts are fixed on vehicles A and B, and thetwo vehicles are subsequently used in the same environment. Then it isdiscovered that one of the four parts replaced on vehicle A has anidentified non-conformance. It can be recommended that the same part onvehicle B be inspected. The system 10 can also provide significantadvantages in reducing down times for systems or vehicles that employlarge numbers of component parts, for example commercial aircraft,marine vessels and rotorcraft. The system 10, when configured fornon-conformance analysis, may prove especially useful in proactivelydetermining which component parts should be inspected. Componentnon-conformances on complex systems such as space vehicles present theadditional challenge that once the space vehicle is launched, it isoften difficult or impossible to repair malfunctioning components. Thus,being able to select suitable components based on a lifecycle of thecomponent in view of other components on the same or similar system orsubsystem, before a spacecraft is launched, provides the opportunity toidentify those components that may be less suitable while the spacecraftis in service, before the spacecraft is launched.

In commercial aircraft manufacturing applications, use of the system 10can help to reduce maintenance times and help the quality engineer tomore optimally select certain components based on their componentlifecycle, even if such components are not associated with the originalwork order for the component. For example, if a work order says toreplace parts A and B, the system can remember that many times when awork order instructs to “replace A and B”, that part C also is replaced.This feature can significantly reduce the down time of the aircraft inan airline's fleet, and reduce the associated lost revenue that resultsfrom aircraft downtime. In this manner, the lifecycle of component partsare used to more efficiently manage the maintenance of a complex systemsuch as an aircraft.

While various embodiments have been described, those skilled in the artwill recognize modifications or variations which might be made withoutdeparting from the present disclosure. The examples illustrate thevarious embodiments and are not intended to limit the presentdisclosure. Therefore, the description and claims should be interpretedliberally with only such limitation as is necessary in view of thepertinent prior art.

1. A non-conformance analysis system, comprising: an associative memorysubsystem populated with information involving a plurality of entitiesdefining different attributes of a component, with each said entitybeing categorized under a user defined entity type, the entities andentity types forming an associative memory; a user input device forenabling a user to input a query concerning said component to obtaininformation useful for managing a lifecycle of said component; and anassociative memory entity analytics engine in communication with saidassociative memory subsystem, and responsive to said user input device,that searches said associative memory using said information provided insaid query to retrieve entities helpful to the user in assessing saidlifecycle of said component.
 2. The system of claim 1, wherein said userinput device comprises a computer display terminal for displayinginformation relating to said retrieved entities.
 3. The system of claim1, further comprising a plurality of different types of informationtools for containing information useful for managing a lifecycle of saidcomponent.
 4. The system of claim 1, wherein said associative memoryentity analytics engine includes query software that uses the queryprovided by the user to perform a plurality of searches of saidassociative memory.
 5. The system of claim 4, wherein said querycomprises a free text query made up of at least one of words andnumbers, and said associative memory entity analytics engine performs asearch of said associative memory for each one of said words and numbersof the free text query.
 6. The system of claim 3, further comprising adatabase update software system for updating said information tools withadditional information created by said user.
 7. The system of claim 3,further comprising a data mining tool in communication with saidinformation tools and said associative learning memory subsystem, saiddata mining tool adapted to obtain specific information from saidinformation tools that is used to create said entities and store saidentities within said associative memory.
 8. The system of claim 3,wherein said information tools comprise at least one of: an historicaldatabase; a transactional database; and a wide area network.
 9. A methodfor forming a system for managing a lifecycle of a component, saidmethod comprising: using an associative memory subsystem populated withinformation involving a plurality of entities, with each said entitybeing categorized under a pre-defined entity type, and the entity typesand entities collectively forming an associative memory; using an inputdevice to enable a user to input a query concerning information relatedto managing a non-conformance of said component; and using an entityanalytics engine in communication with said input device and saidassociative memory subsystem to analyze entities stored in saidassociative memory and to retrieve specific ones of said entities storedin said associative memory that include information helpful to said userin managing a non-conformance of said component.
 10. The method of claim9, wherein said using an entity analytics engine comprises using anentity analytics engine having software that uses the query provided bythe user to perform a plurality of searches of said associative memory.11. The method of claim 10, wherein said plurality of searches comprisesindependent searches for at least one of: different words used in saidquery; and different numbers used in said query.
 12. The method of claim9, wherein said using an input device to enable a user to input a queryconcerning information related to managing a non-conformance of saidcomponent comprises using an input device that enables said user toinput a free text query to said entity analytics engine, said free textquery including at least one of words and numbers used to form said freetext query.
 13. The method of claim 9, wherein said specific ones ofsaid entities include specific information helpful to determining alifecycle of component.
 14. The method of claim 9, further comprisingusing information from a plurality of different information sources toform said entities.
 15. The method of claim 14, wherein forming saidentities using said different information sources comprises at least oneof: forming said entities with information obtained from an historicaldatabase; forming said entities with information obtained from atransactional database; and forming said entities with informationobtained from a wide area network.
 16. The method of claim 15, furthercomprising enabling said user to update said information tools withadditional information created by said user.
 17. The method of claim 9,further comprising displaying said information associated with saidentities on said input device.
 18. A method for managing a lifecycle ofa component, comprising: providing an associative memory subsystemhaving an associative memory; said associative memory having informationrelating to said component, said information being organized inaccordance with pre-defined entity types, where each said entity typehas at least one entity representing a specific attribute; using anentity analytics engine to search said associative memory usinginformation provided from a user through a query; using said entityanalytics engine during said search to obtain all relevant entitiescontaining attributes that may be related to said information in saidquery; and outputting said relevant entities to said user for analysisby said user.
 19. The method of claim 18, wherein said outputting saidrelevant entities to said user comprises outputting said relevantentities to a user input device that is able to display said relevantentities to said user.
 20. The method of claim 18, wherein said using anentity analytics engine to search said associative memory usinginformation in said query from said user comprises using free textinformation provided by said user from a user input device.
 21. Themethod of claim 18, wherein said using said entity analytics engine toobtain all relevant entities containing attributes that may be relatedto said information in said query comprises using said entity analyticsengine to obtain all of said entities that relate to objects identicalto said component and objects similar in at least one of: form to saidcomponent; function to said component; and lifecycle to said component.